{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2f003044",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a href=\"https://colab.research.google.com/github/nyandwi/machine_learning_complete/blob/main/6_classical_machine_learning_with_scikit-learn/5_decision_trees_for_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf67852e",
   "metadata": {},
   "source": [
    "*This notebook was created by [Jean de Dieu Nyandwi](https://twitter.com/jeande_d) for the love of machine learning community. For any feedback, errors or suggestion, he can be reached on email (johnjw7084 at gmail dot com), [Twitter](https://twitter.com/jeande_d), or [LinkedIn](https://linkedin.com/in/nyandwi).*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c5ba4fe",
   "metadata": {},
   "source": [
    "<a name='0'></a>\n",
    "# Decision Trees - Intro and Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07494ffb",
   "metadata": {},
   "source": [
    "Decision Trees are supervised machine learning algorithms that are used for both regression and classification tasks. Trees are powerful algorithms that can handle complex datasets. \n",
    "\n",
    "Here are 7 interesting facts about decision trees:\n",
    "\n",
    "* They do not need the numerical input data to be scaled. Whatever the numerical values are, decision trees don't care. \n",
    "\n",
    "* Decision trees handle categorical features in the raw text format (Scikit-Learn doesn't support this, TensorFlow's trees implementation does).\n",
    "\n",
    "* Different to other complex learning algorithms, the results of decision trees can be interpreted. It's fair to say that decision trees are not blackbox type models. \n",
    "* While most models will suffer from missing values, decision trees are okay with them.\n",
    "* Trees can handle imbalanced datasets. You will only have to adjust the weights of the classes.\n",
    "* Trees can provide the feature importances or how much each feature contributed to the model training results.\n",
    "* Trees are the basic building blocks of ensemble methods such as random forests and gradient boosting machines.\n",
    "\n",
    "The way decision trees works is like the series of if/else questions. Let's say that you want to make a decision of the car to buy. In order to get the right car to buy, you could go on and evaluate the level of the safety, the number of sits and doors by asking series of if like questions. \n",
    "\n",
    "Here is the structure of the decision trees.\n",
    "\n",
    "![Decision Trees.png](https://cdn.hashnode.com/res/hashnode/image/upload/v1632212349927/4XijLSXib.png)\n",
    "\n",
    "\n",
    "A well-known downside of decision trees is that they tend to overfit the data easily(pretty much assumed they will always overfit at first). One way to overcome overfitting is to reduce the maximum depth of the decision tree (refered to as `max_depth`hyperparameter) in decision trees. We will see other techniques to avoid overfitting. \n",
    "\n",
    "To motivate the superpower of decision trees, let's use it for a regression task where instead of predicting class, we are predicting a continous value. In the next lab, we will use them for classification. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4e2c5a6",
   "metadata": {},
   "source": [
    "## Decision Trees for Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae5e8372",
   "metadata": {},
   "source": [
    "### Contents\n",
    "\n",
    "* [1 - Imports](#1)\n",
    "* [2 - Loading the data](#2)\n",
    "* [3 - Exploratory Analysis](#3)\n",
    "* [4 - Preprocessing the data](#4)\n",
    "* [5 - Training Decision Trees](#5)\n",
    "* [6 - Evaluating Decision Trees](#6)\n",
    "* [7 - Improving Decision Trees](#7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99058d12",
   "metadata": {},
   "source": [
    "<a name='1'></a>\n",
    "## 1 - Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "df278c5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35699c38",
   "metadata": {},
   "source": [
    "<a name='2'></a>\n",
    "\n",
    "## 2 - Loading the data\n",
    "\n",
    "In this regression task with decision trees, we will use the Machine CPU (Central Processing Unit) data which is avilable at [OpenML](https://www.openml.org/t/5492). We will load it with Sklearn `fetch_openml` function. \n",
    "\n",
    "If you are reading this, it's very likely that you know CPU or you have once(or many times) thought about it when you were buying your computer. In this notebook, we will predict the relative performance of the CPU given the following data: \n",
    "\n",
    "* MYCT: machine cycle time in nanoseconds (integer)\n",
    "* MMIN: minimum main memory in kilobytes (integer)\n",
    "* MMAX: maximum main memory in kilobytes (integer)\n",
    "* CACH: cache memory in kilobytes (integer)\n",
    "* CHMIN: minimum channels in units (integer)\n",
    "* CHMAX: maximum channels in units (integer)\n",
    "* PRP: published relative performance (integer) (target variable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "67905ee6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's hide warnings\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d3083946",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "\n",
    "machine_cpu = fetch_openml(name='machine_cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "46c09852",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.utils.Bunch"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(machine_cpu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "154ed67e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(209, 6)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "machine_cpu.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "69e438fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**Author**:   \n",
      "**Source**: Unknown -   \n",
      "**Please cite**:   \n",
      "\n",
      "The problem concerns Relative CPU Performance Data. More information can be obtained in the UCI Machine\n",
      " Learning repository (http://www.ics.uci.edu/~mlearn/MLSummary.html).\n",
      " The used attributes are :\n",
      " MYCT: machine cycle time in nanoseconds (integer)\n",
      " MMIN: minimum main memory in kilobytes (integer)\n",
      " MMAX: maximum main memory in kilobytes (integer)\n",
      " CACH: cache memory in kilobytes (integer)\n",
      " CHMIN: minimum channels in units (integer)\n",
      " CHMAX: maximum channels in units (integer)\n",
      " PRP: published relative performance (integer) (target variable)\n",
      " \n",
      " Original source: UCI machine learning repository. \n",
      " Source: collection of regression datasets by Luis Torgo (ltorgo@ncc.up.pt) at\n",
      " http://www.ncc.up.pt/~ltorgo/Regression/DataSets.html\n",
      " Characteristics: 209 cases; 6 continuous variables\n",
      "\n",
      "Downloaded from openml.org.\n"
     ]
    }
   ],
   "source": [
    "print(machine_cpu.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d7180991",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MYCT', 'MMIN', 'MMAX', 'CACH', 'CHMIN', 'CHMAX']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying feature names\n",
    "\n",
    "machine_cpu.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e3ba092e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MYCT</th>\n",
       "      <th>MMIN</th>\n",
       "      <th>MMAX</th>\n",
       "      <th>CACH</th>\n",
       "      <th>CHMIN</th>\n",
       "      <th>CHMAX</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>125.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>269.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>172.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>124.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>98.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>125.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>480.0</td>\n",
       "      <td>512.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>480.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>209 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      MYCT    MMIN     MMAX   CACH  CHMIN  CHMAX  class\n",
       "0    125.0   256.0   6000.0  256.0   16.0  128.0  198.0\n",
       "1     29.0  8000.0  32000.0   32.0    8.0   32.0  269.0\n",
       "2     29.0  8000.0  32000.0   32.0    8.0   32.0  220.0\n",
       "3     29.0  8000.0  32000.0   32.0    8.0   32.0  172.0\n",
       "4     29.0  8000.0  16000.0   32.0    8.0   16.0  132.0\n",
       "..     ...     ...      ...    ...    ...    ...    ...\n",
       "204  124.0  1000.0   8000.0    0.0    1.0    8.0   42.0\n",
       "205   98.0  1000.0   8000.0   32.0    2.0    8.0   46.0\n",
       "206  125.0  2000.0   8000.0    0.0    2.0   14.0   52.0\n",
       "207  480.0   512.0   8000.0   32.0    0.0    0.0   67.0\n",
       "208  480.0  1000.0   4000.0    0.0    0.0    0.0   45.0\n",
       "\n",
       "[209 rows x 7 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the whole dataframe\n",
    "\n",
    "machine_cpu.frame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88b6b4ae",
   "metadata": {},
   "source": [
    "Now, let's get the data and labels. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "59f873c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "machine_data = machine_cpu.data\n",
    "machine_labels = machine_cpu.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ef5bf335",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(machine_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2f05b049",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(machine_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c492fd93",
   "metadata": {},
   "source": [
    "<a name='3'></a>\n",
    "## 3 - Exploratory Analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9906083",
   "metadata": {},
   "source": [
    "Before doing exploratory analysis, let's get the training and test data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "98d74144",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The size of training data is: 167 \n",
      "The size of testing data is: 42\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(machine_data,machine_labels, test_size=0.2,random_state=20)\n",
    "\n",
    "print('The size of training data is: {} \\nThe size of testing data is: {}'.format(len(X_train), len(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3fc640e",
   "metadata": {},
   "source": [
    "Let's visualize the histograms of all numeric features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "04856278",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train.hist(bins=50, figsize=(15,10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eca7747",
   "metadata": {},
   "source": [
    "Or we can quickly use `sns.pairplot()` to look into the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "803a1d12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7fa53396ab90>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1080x1080 with 42 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "753bb539",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MYCT</th>\n",
       "      <th>MMIN</th>\n",
       "      <th>MMAX</th>\n",
       "      <th>CACH</th>\n",
       "      <th>CHMIN</th>\n",
       "      <th>CHMAX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>207.958084</td>\n",
       "      <td>2900.826347</td>\n",
       "      <td>11761.161677</td>\n",
       "      <td>26.071856</td>\n",
       "      <td>4.760479</td>\n",
       "      <td>18.616766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>266.772823</td>\n",
       "      <td>4165.950964</td>\n",
       "      <td>12108.332354</td>\n",
       "      <td>42.410014</td>\n",
       "      <td>6.487439</td>\n",
       "      <td>27.489919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>110.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>8000.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>232.500000</td>\n",
       "      <td>3100.000000</td>\n",
       "      <td>16000.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1500.000000</td>\n",
       "      <td>32000.000000</td>\n",
       "      <td>64000.000000</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>176.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              MYCT          MMIN          MMAX        CACH       CHMIN  \\\n",
       "count   167.000000    167.000000    167.000000  167.000000  167.000000   \n",
       "mean    207.958084   2900.826347  11761.161677   26.071856    4.760479   \n",
       "std     266.772823   4165.950964  12108.332354   42.410014    6.487439   \n",
       "min      17.000000     64.000000     64.000000    0.000000    0.000000   \n",
       "25%      50.000000    768.000000   4000.000000    0.000000    1.000000   \n",
       "50%     110.000000   2000.000000   8000.000000    8.000000    2.000000   \n",
       "75%     232.500000   3100.000000  16000.000000   32.000000    6.000000   \n",
       "max    1500.000000  32000.000000  64000.000000  256.000000   52.000000   \n",
       "\n",
       "            CHMAX  \n",
       "count  167.000000  \n",
       "mean    18.616766  \n",
       "std     27.489919  \n",
       "min      0.000000  \n",
       "25%      5.000000  \n",
       "50%      8.000000  \n",
       "75%     24.000000  \n",
       "max    176.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking summary stats\n",
    "\n",
    "X_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f0e4fb67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MYCT     0\n",
       "MMIN     0\n",
       "MMAX     0\n",
       "CACH     0\n",
       "CHMIN    0\n",
       "CHMAX    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking missing values\n",
    "\n",
    "X_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4977a1a1",
   "metadata": {},
   "source": [
    "We don't have any missing values. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6733727",
   "metadata": {},
   "source": [
    "<a name='4'></a>\n",
    "\n",
    "## 4 - Data Preprocessing \n",
    "\n",
    "It is here that we prepare the data to be in the proper format for the machine learning model. \n",
    "\n",
    "Decision trees don't care if the features are scaled or not. Let's set up a pipeline to scale features, and we will use it to verify that notion. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b7ee6d06",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "scale_pipe = Pipeline([\n",
    "    ('scaler', StandardScaler())\n",
    "    \n",
    "])\n",
    "\n",
    "X_train_scaled = scale_pipe.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd496de6",
   "metadata": {},
   "source": [
    "<a name='5'></a>\n",
    "\n",
    "## 5 - Training Decision Tree Regressor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8311b87a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg = DecisionTreeRegressor()\n",
    "\n",
    "tree_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "757c0a01",
   "metadata": {},
   "source": [
    "Let's train the same model on the scaled data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ea28474e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_reg_scaled = DecisionTreeRegressor()\n",
    "\n",
    "tree_reg_scaled.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9afb9bde",
   "metadata": {},
   "source": [
    "<a name='6'></a>\n",
    "\n",
    "## 6 - Evaluating Decision Trees\n",
    "\n",
    "Let's first check the root mean squarred errr on the training. It is not advised to evaluate the model on the test data since we haven't improved it yet. I will make a function to make it easier and to avoid repetitions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6962359e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "def predict(input_data,model,labels):\n",
    "    \"\"\"\n",
    "    Take the input data, model and labels and return predictions\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    preds = model.predict(input_data)\n",
    "    mse = mean_squared_error(labels,preds)\n",
    "    rmse = np.sqrt(mse)\n",
    "    rmse\n",
    "    \n",
    "    return rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a17fed16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.724590719956222"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict(X_train, tree_reg, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "109b17a0",
   "metadata": {},
   "source": [
    "And when the training data is scaled..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c943c75d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.724590719956222"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict(X_train_scaled, tree_reg_scaled, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7a78d94",
   "metadata": {},
   "source": [
    "As you can see, there is no difference at all. So in your future projects using decision trees, whether you scale the data or not, your predictions will not be affected. \n",
    "\n",
    "But don't do this...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d2b92b25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "201.29511805033474"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict(X_train_scaled, tree_reg, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db9fd8d8",
   "metadata": {},
   "source": [
    "If you can inspect the parameters that I passed in predict function above, there are scaled data, model trained on unscaled data, and labels(y_train). As you may have guessed, that is a complete mismatch which led to poor results. If you trained a model on scaled data, the data that you predict the model on should be scaled in the same way too. \n",
    "\n",
    "Let's try to improve the model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66a574a5",
   "metadata": {},
   "source": [
    "<a name='7'></a>\n",
    "\n",
    "## 7 - Improving Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0f92566d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ccp_alpha': 0.0,\n",
       " 'criterion': 'mse',\n",
       " 'max_depth': None,\n",
       " 'max_features': None,\n",
       " 'max_leaf_nodes': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_impurity_split': None,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'random_state': None,\n",
       " 'splitter': 'best'}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_reg.get_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af110f75",
   "metadata": {},
   "source": [
    "We have seen that one way to improve the decision tree model is to find the right number of max_depth and some other few parameters. Let's use GridSearch to find best hyperparameters. Note that this may end up overfitting because we have a small dataset. But since we are doing this for learning purpose (you can adapt it to your problem), let's do it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "056818ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 250 candidates, totalling 750 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, estimator=DecisionTreeRegressor(random_state=42),\n",
       "             param_grid={'max_depth': [None, 0, 1, 2, 3],\n",
       "                         'max_leaf_nodes': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "                         'min_samples_split': [0, 1, 2, 3, 4]},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "params_grid = {'max_leaf_nodes': list(range(0, 10)), 'min_samples_split': [0,1,2, 3, 4], \n",
    "              'max_depth':[None,0,1,2,3]}\n",
    "\n",
    "#refit is true by default. The best estimator is trained on the whole dataset \n",
    "\n",
    "grid_search = GridSearchCV(DecisionTreeRegressor(random_state=42), params_grid, verbose=1, cv=3, refit=True)\n",
    "\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "55a5d7f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': None, 'max_leaf_nodes': 9, 'min_samples_split': 4}"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c4a1488d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_leaf_nodes=9, min_samples_split=4, random_state=42)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4995ab2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_best = grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44fc64ef",
   "metadata": {},
   "source": [
    "Let's make prediction on the training data again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "85b1b211",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34.999530266023044"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict(X_train, tree_best, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b93b6002",
   "metadata": {},
   "source": [
    "Like we said, we overfitted. The RMSE on our small dataset was pretty enough (and you probably would not use decision trees for small data like this we are using). \n",
    "\n",
    "Finally we can evaluate the model on the test set. Let's use the orginal model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f03cc7fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42.89411103991738"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict(X_test, tree_reg, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f5ebfcd",
   "metadata": {},
   "source": [
    "Clearly, the model overfitted and the improvements we tried didn't improve anything. This is not an issue of the model or our approach to improve the model. Decision trees are complex models for the data we have. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af266a08",
   "metadata": {},
   "source": [
    "This is the end of the notebook. We have learned the fundamental idea behind the decision trees, and used it to predict the CPU performance. In the next lab, we will use it for classification task and we will use a real world dataset so that we can practically improve the decision trees model. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17af5728",
   "metadata": {},
   "source": [
    "[Back to top](#0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "620a4d3e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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